Estimation of efficiency of measurement of digital camera photosensor noise by automatic segmentation of non-uniform target method and the standard EMVA 1288

2021 ◽  
pp. 28-35
Author(s):  
Nickolay N. Evtikhiev ◽  
Alexander V. Kozlov ◽  
Vitaly V. Krasnov ◽  
Vladislav G. Rodin ◽  
Rostislav S. Starikov ◽  
...  
Keyword(s):  
Machine Vision ◽  
Automatic Segmentation ◽  
Digital Camera ◽  
Noise Estimation ◽  
Estimation Methods ◽  
Accuracy Improvement ◽  
Experimental Realization ◽  
Temporal Noise ◽  
Noise Characteristics ◽  
Accuracy Of Measurements

In this paper important task of estimation of digital camera’s noise parameters is considered. Relation of accuracy of data obtained with digital camera and photosensor noise is discussed. Both standard European machine vision association EMVA 1288 and fast automatic segmentation of non-uniform target (ASNT) noise estimation methods are compared. Noise characteristics of machine vision PixeLink PL-B781F, scientific Retiga R6 and amateur mirrorless Canon EOS M100 cameras have been investigated. Accuracy of measurements, speed of calculation and experimental realization has been analyzed. Accuracy of temporal noise estimation by modified ASNT method is no less than that one for standard EMVA 1288. But the ASNT method can be implemented much faster than the standard EMVA 1288 even with additional frames for accuracy improvement.

scholarly journals A method for measuring digital camera noise by automatic segmentation of a striped target

2021 ◽  
Vol 45 (2) ◽  
pp. 267-276
Author(s):  
N.N. Evtikhiev ◽  
A.V. Kozlov ◽  
V.V. Krasnov ◽  
V.G. Rodin ◽  
R.S. Starikov ◽  
...  
Keyword(s):  
Measurement Error ◽  
Standard Method ◽  
Shot Noise ◽  
Automatic Segmentation ◽  
Digital Camera ◽  
Experimental Results ◽  
Signal Segmentation ◽  
Temporal Noise ◽  
Photo Response ◽  
Dark Signal

Currently, cameras are widely used in scientific, industrial and amateur tasks. Thus, one needs to be able to quickly evaluate characteristics and capabilities of a particular camera. A method for measuring noise components of the camera photosensor is proposed. It allows one to estimate shot noise, dark temporal noise, photo response non-uniformity and dark signal non-uniformity. For noise measurement, just two images of the same scene need to be registered. The scene consists of several stripes (quasihomogeneous regions). Then the images are processed by automatic signal segmentation. The performance and accuracy of the proposed method are higher than or equal to other fast methods. The experimental results obtained are similar to those derived using a time-consuming standard method within a measurement error.

scholarly journals Exposure to Road, Railway, and Aircraft Noise and Arterial Stiffness in the SAPALDIA Study: Annual Average Noise Levels and Temporal Noise Characteristics

2017 ◽  
Vol 125 (9) ◽  
pp. 097004 ◽  
Author(s):  
Maria Foraster ◽  
Ikenna C. Eze ◽  
Emmanuel Schaffner ◽  
Danielle Vienneau ◽  
Harris Héritier ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Aircraft Noise ◽  
Noise Levels ◽  
Annual Average ◽  
Temporal Noise ◽  
Noise Characteristics

scholarly journals Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 339 ◽  
Author(s):  
Yongsong Li ◽  
Zhengzhou Li ◽  
Kai Wei ◽  
Weiqi Xiong ◽  
Jiangpeng Yu ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Haar Wavelet ◽  
Image Sensor ◽  
Median Absolute Deviation ◽  
Noise Estimation ◽  
Estimation Methods ◽  
Absolute Deviation

Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

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